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Copyright © 2013 by Modern Scientific Press Company, Florida, USA
International Journal of Environment and Bioenergy, 2013, 7(2): 91-107
International Journal of Environment and Bioenergy
Journal homepage: www.ModernScientificPress.com/Journals/IJEE.aspx
ISSN: 2165-8951
Florida, USA
Article
Biosorption of Toxic Heavy Metals by Unmodified Marine Red
Alga (Kappaphycus alvarezii): Kinetics and Isotherm Studies
Sohail Rafiq 1, Majid K.M. Ali 2, Mahyar Sakari 1, Jumat Sulaiman 2, Suhaimi M. Yasir 3, *
1Water Research Unit (WRU), School of Science and Technology, Universiti Malaysia Sabah, 88400
Kota Kinabalu, Sabah 2Mathematics with Economics Programme, School of Science and Technology, Universiti Malaysia
Sabah, 88400 Kota Kinabalu, Sabah 3Seaweed Research Unit (UPRL), School of Science and Technology, Universiti Malaysia Sabah,
88400 Kota Kinabalu, Sabah
* Author to whom correspondence should be addressed; E-Mail: ysuhaimi@ums.edu.my; Tel.:
+6088-320000 ext: 5778.
Article history: Received 5 July 2013, Received in revised form 13 August 2013, Accepted 30 August
2013, Published 15 October 2013.
Abstract: Present study has been undertaken to propose an alternative use of unmodified
marine red alga (Kappaphycus alvarezii) (UMRA) as biosorbent for the removal of heavy
metal ions from aqueous solutions. The biosorption studies were conducted in batch
adsorption system as a function of contact time and initial metal ion concentration. The
adsorption system attained equilibrium after 150 min of contact time for Pb (II), Cu (II), Zn
(II) and Cd (II). The removal efficiency of red alga improved as metal ions concentrations
were lowered. The equilibrium sorption data was better explained by Langmuir isotherm
model suggesting that the adsorption of metal cations observed monolayer sorption pattern.
Pseudo-first-order model, pseudo-second-order model and intraparticle diffusion model
were utilised to test the sorption kinetics involved in the process. It was observed that
pseudo-first-order kinetic model could better describe the adsorption kinetics. A
comparison of maximum sorption capacity of several agro-based waste material showed
that red alga can be a suitable alternative to use as biosorbent in the removal of toxic heavy
metals from aqueous solutions.
Keywords: biosorption; unmodified; removal; isotherm; kinetics.
Int. J. Environ. Bioener. 2013, 7(2): 91-107
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1. Introduction
The heavy metal pollution has become a major environmental problem in the past few decades
after industrial revolution. With the rapid development of industries such as metal plating facilities,
mining operations, fertilizer industries, tanneries, batteries, paper industries and pesticides, etc., toxic
heavy metals are directly or indirectly are being discharged into the aquatic streams posing the genuine
environmental threat. Contrary to organic pollutants, heavy metals are non-biodegradable and tend to
accumulate in living bodies and some are carcinogenic in nature. Toxic heavy metals found in
industrial and domestic wastewaters including lead, copper, cadmium, zinc, nickel, mercury and
chromium. The toxic heavy metals in the aquatic world are extremely deleterious for fauna and flora.
Most of the research is diverted to developing cost-effective technologies for the removal of metal ions
from aqueous solutions. Common wastewater treatment technologies included membrane separation,
electrochemical precipitation, ion exchange, pre-concentration, flotation, membrane filtration,
ultrafiltration, coagulation-flocculation and adsorption. Adsorption is the most commonly used
technique because of its user friendliness and cost effectiveness. This process is found to be versatile
and effective when it comes to the operational cost and appropriate regeneration steps. Several recent
published works utilized locally available low cost adsorbents including sago waste (Quek et al.,
1998), neem bark (Naiya et al., 2008), orange peels (Xuan et al., 2006), Banana waste (Annadurai et
al., 2006), coffee residue (Boonamnuayvitaya et al., 2004), tree fern (Ho et al., 2004), tea waste
(Amarasinghe and Williams, 2007), rice husk (Feng et al., 2004), peanut hulls (Brown et al., 2000) and
barley straw (Larsen and Schierup, 1981).
Since UMRA is abundant, locally available, cost effective and readily available media which
can be utilized as biosorbent material for wastewater treatment processes. Present study focuses on the
potential utilization of locally available material to remove toxic heavy metals from waste water.
2. Materials and Methods
2.1. Sample Preparation
The UMRA samples were collected from the east coast of Sabah, Malaysia. The samples were
thoroughly washed with distilled water to remove the impurities and solar dried for 16 h. The samples
were grounded to obtain the 1-2 mm of particle size fraction for batch experiments. The samples were
stored in airtight glass containers without further treatment for prior analysis.
2.2. Solution Preparation
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93
The Pb (II), Cu (II), Zn (II) and Cd (II) ionic solutions were prepared by mixing the nitrate salts
with distilled water to get 1000 mg/L ionic concentration. Working solutions were prepared by diluting
the stock solutions with distilled water to get the desired ionic concentrations.
2.3. Surface Morphology Analysis
The surface morphology of UMRA was determined by using Carl/Zeiss Evo MA 10 scanning
electron microscope (SEM). The sample was mounted onto 1.27 cm diameter stub and was coated with
gold using Emitech K550X Sputter Coater at 20 mA current for 1 min duration. Images were captured
under electron acceleration voltage of 15 kv.
2.4. Fourier Transform Infrared Spectroscopy
The UMRA sample was grounded to powder by using mortar and pestle. The powdered sample
was dried in the drying oven for 30 min at 60 °C to get rid of water molecules. Perkin Elmer series
spectrum 100 was used to obtain the spectrum in the range of 4000 to 650 cm-1 in 4 cm-1 resolution.
2.5. Isotherm Experiments
Batch adsorption isotherm experiments were conducted by equilibrating 0.1 g of sorbent with
100 mL of each of four different metal ion solutions with known concentrations at pH 5 and 22 °C ± 2
°C in an orbital shaker for the known period of time. The mixtures were then removed, filtered and
analysed for left over heavy metal content with atomic absorption spectrophotometer (Perkin Elmer
4100).
2.6. Kinetic Studies
Kinetic study was conducted with known amount of sorbent with 100 mL of each metal ion
solutions at pH 5. The samples were shaken at an agitation rate of 150 rpm and then were taken out
after known time intervals, filtered and analysed for heavy metal content.
3. Results and Discussion
3.1. Surface Morphology
The SEM analysis of UMRA (Figure 1) shows protuberance and irregular surface. The
characteristic microstructure can be seen on surface of sorbent, which may be due to calcium and other
crystalloid salts. Characteristic porous cavities responsible for adsorption can be seen on the surface of
sorbent.
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Figure 1. Scanning electron micrograph (SEM) of UMRA
3.2. Fourier Transform Infrared Spectroscopy
The FTIR spectrum of the UMRA can be seen in figure 2. The FTIR spectroscopic analysis
indicated broad bands at 3377 cm−1, representing bonded –OH and –NH groups. The two
characteristic bands observed at 1633 and 1413 cm−1 are because of carboxylate ions and can be related
to strong asymmetrical stretching and weak symmetrical stretching, respectively. The bands at about
1236 cm−1, represents –SO3 stretching whereas bands around 1029 cm−1 assigned to the –C–O
stretching of alcoholic groups. Functional groups including carbonyl, hydroxyl, nitro and ethers are
responsible to adsorb heavy metals (Srivastava et al., 2006; Demirbas, 2008; Li et al., 2008).
4000.0 3600 3200 2800 2400 2000 1800 1600 1400 1200 1000 800 660.0
94.16
94.4
94.6
94.8
95.0
95.2
95.4
95.6
95.8
96.0
96.2
96.4
96.6
96.8
97.0
97.2
97.4
97.6
97.8
98.0
98.2
98.4
98.6
98.8
99.0
99.2
99.4
99.6
99.8
100.00
cm-1
%T
3377.03
1632.91 1029.26
887.88
1236.781413.21 818.50
Figure 2. FT-IR spectroscopy of UMRA
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3.3. Effect of Initial Metal Concentration
The initial metal concentration has strong effect on adsorption capability of various sorbents.
Normally, removal capacity of sorbent increased with increased in initial metal ion concentration in
solution (Ahmaruzzaman, 2011). The initial concentration of metal ions provides an important driving
force to overcome all mass transfer resistance of heavy metals between the aqueous and solid phases
(Chowdhury and Saha, 2010). Results showed that increase in initial metal concentration from 5 mg/L
to 100 mg/L showed the decrease in percentage removal of Pb (II), Cu (II), Zn (II) and Cd (II) ions
from 26.8 % to 3.7 %, 22.4 % to 3.3 %, 20.8 % to 3.1 % and 19.0 % to 2.9 %, respectively (Figure 3).
However, the adsorptions in terms of mg/g were increased by increasing initial metal ion
concentration.
Figure 3. Effect of initial concentration of metal cations on adsorption at 22 °C ± 2 °C
3.4. Effect of Contact Time
The rate of adsorption is of the most important parameters to design batch adsorption
experiments. Consequently, it is important to establish the time dependence of such systems under
various process conditions. Metal ion adsorption capacities were determined as a function of time to
optimize the contact time for the adsorption of heavy metals on MRA. The variation of metal
adsorption with respect to time is shown in Figure 4. It can be concluded that the removal of metal ions
is increasing with increase in the contact time. Initially the adsorption was low but increases with the
passage of time. The metal removal of Pb(II), Cu (II), Zn (II) and Cd (II) at 90 min were found to be
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20.4 %, 14.7, 12.24 and 11.8 %, respectively. The optimum time required for UMRA to get the
maximum adsorption efficiency was found to be 150 min.
Figure 4. Effect of contact time on adsorption of metal cations
3.5. Isotherm Studies
Adsorption is generally described through adsorption isotherms, which is the relationship
between the amounts of a substance adsorbed at constant temperature (Ahmad et al., 2007). According
to Özcar et al. (2008) data collected from isotherm can be used to predict the maximum adsorption
capacity and to optimise the usage of sorbent. Two most commonly known adsorption isotherm
models, namely Langmuir (1918) and Freundlich (1906) were tested for fitting the experimental data.
3.5.1. Langmuir isotherm
The Langmuir adsorption isotherm relates the adsorption of adsorbate on a solid surface based
on the assumption that a maximum adsorption capacity corresponds to a saturated monolayer of solute
molecules on the adsorbent surface. There are three assumptions adapted for this isotherm, a)
adsorption is only occur for monolayer coverage; b) all the active sites on the surface of the sorbent are
same and can accommodate one sorbent specie and c) the ability of a specie to be adsorbed on a
random site is independent. The Langmuir adsorption isotherm can be represented in the equation
below.
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eL
eLme
CK
CKQQ
1 (1)
where, Qe (mg/g) is the amount of metal ions adsorbed at equilibrium, Qm (mg/g) is the maximum
adsorption capacity of the adsorbent, KL (L/mg) is the Langmuir constant and Ce (mg/L) is the
concentration of sorbate ions at equilibrium. The linearized Langmuir isotherm of Ce versus Ce/Qe
produced a straight line (Figure 5) with slope 1/Qm and intercept 1/QmKL. Table 1 lists the Langmuir
constant and maximum adsorption capacity Qm of different metal ions onto UMRA at 22 ± 2 °C.
Figure 5. Linearised Langmuir isotherm model for adsorption of metal cations
Table 1. Summary of Langmuir and Freundlich constants
Langmuir Qm
(mg/g)
KL
(L/mg) R2
Pb (II) 3.994 0.150 0.999
Cu (II) 3.534 0.138 0.999
Zn (II) 3.432 0.116 0.999
Cd (II) 3.230 0.115 0.999
Freundlich KF
(mg/g (L/mg)1/n) n R2
Pb (II) 1.101 3.250 0.910
Cu (II) 0.716 3.110 0.897
Zn (II) 0.535 2.957 0.925
Cd (II) 0.428 2.874 0.913
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The essential characteristics of the Langmuir isotherm can be expressed in terms of
dimensionless constant separation factor RL given by the following equation:
oL
LCK
R
1
1 (2)
where KL is the Langmuir constant and Co is the highest initial metal ion concentration (mg/L) used in
the experiment. According to the value of RL the isotherm shape can be interpreted as follows.
Value of RL Type of adsorption
RL > 1 Unfavourable
RL = 1 Linear
0 < RL < 1 Favourable
RL = 0 Irreversible
The values of RL (Figure 6) calculated were in range between 0 and 1 which indicate that the
adsorption is favourable at experimental conditions studied.
Figure 6. The separation factor RL derived from Langmuir model against different
initial metal concentrations
The reverse fitting graph of Langmuir isotherm (Figure 7) showed in agreement with
experimental and calculated data of adsorption of heavy metal cations onto UMRA.
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99
Figure 7. Reverse fitting graph of Langmuir and Freundlich model
Table 2. Comparison of maximum adsorption capacities reported with present work
Media
used
Qmax
(mg/g) Reference
Media
used
Qmax
(mg/g) Reference
Pb (II)
Cu (II)
Barley
straw 15.2
Larsen and Schierup,
1981
Wheat
shell 8.3 Basci et al., 2004
Rice husk 11.0 Chuah et al., 2005 Peanut
hull 8.0 Brown et al., 2000
Olive stone
waste 9.2 Fiol et al., 2006
Coffee
husks 7.5 Oliveira et al., 2008
Orange
peel 4.0 Annadurai et al., 2003
Banana
peel 4.8 Annadurai et al., 2003
Hazel nut
shell 1.78 Cimino et al., 2000
Orange
peel 3.7 Annadurai et al., 2003
UMRA 4.0 Present study UMRA 3.5 Present study
Zn (II) Cd (II)
Papaya
wood 13.5 Namane et al., 2005
Olive
cake 10.6 Doyurum and Çelik, 2006
Peanut
hull 9 Basci et al., 2004
Rice
polish 9.7 Singh et al., 2005
Coir 8.6 Conrad and Hansen,
2007 Rice husk 8.6
Kumar and Bandyopadhyay et al.,
2006
Coffee
husks 5.6
Oliveira et al.,
2008
Coffee
husks 6.9
Oliveira et al.,
2008
Cocoa
shell 2.9 Meunier et al., 2003
Wheat
bran 0.7 Singh et al., 2006
UMRA 3.4 Present study UMRA 3.2 Present study
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3.5.2. Freundlich isotherm
The Freundlich adsorption isotherm relates the adsorption of adsorbate ions on a solid surface
based on the assumption that adsorption occurs on heterogeneous surface. The Freundlich adsorption
isotherm can be represented in the equation as under;
/n
eFe CKQ1
(3)
where, Qe is the amount of metal ions adsorbed at equilibrium, KF is the Freundlich adsorption
capacity constant, Ce is the concentration of sorbate ions at equilibrium and n is the Freundlich
intensity constant. The lineraised plot of log Ce versus log Qe yields a straight line (Figure 8). The
slope 1/n ranging between 0 and 1 is a measure of adsorption intensity or surface heterogeneity,
becoming more heterogeneous as its value gets closer to zero. The Freundlich constants with
correlation coefficient are presented in Table 1.
The correlation coefficients obtained from Langmuir and Freundlich models suggest that the
Langmuir isotherm model explains better the experimental data. Moreover, the reverse fitting of
Langmuir model further confirmed the suitability of this model for the experimental data referring that
the adsorption takes place in monolayer fashion.
Figure 8. Freundlich isotherm model for the adsorption of metal cations
3.6. Adsorption Kinetics
In order to investigate the mechanism of adsorption, the pseudo-first-order, pseudo-second-
order and intraparticle diffusion equations were used to test the experimental data.
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3.6.1. Pseudo-first-order
The pseudo-first-order model is one of the most common kinetic models widely used to analyse
the adsorption of metal cations onto different adsorbent media. The pseudo-first-order model assumes
that the rate of adsorption on sorbent is proportional to the number of active sites available on to
adsorbent media. This model can be represented by the following equation:
tk
QQQ ete303.2
log)log( 1 (4)
where Qe and Qt are the amounts of metal ions adsorbed on adsorbent at equilibrium and at time t,
respectively (mg/g). The slope and intercept of plot of log (Qe-Qt) versus t were used to determine the
pseudo-first-order rate constant k1 (min-1) (Figure 9).
Figure 9. Pseudo-first-order kinetic model for the adsorption of metal cations
3.6.2. Pseudo-second-order
The pseudo-second-order model can be expressed as
eet Q
t
QkQ
t
2
2
1 (5)
where, k2 (g/mg min) is the pseudo second order rate constant. If the pseudo-second-order kinetics is
applicable, the plot t/Qt versus t shows a linear relationship. The k2 and Qe (mg/g) can be determined
from the intercept and slope of the graph (Figure 10). The summary of constants calculated from
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102
pseudo-first-order, pseudo-second-order and intraparticle diffusion model can be seen in Table 3. The
initial sorption rate h of Pb (II) ions was higher as compared to Cu (II), Zn (II) and Cd (II) ions.
Figure 10. Pseudo-second-order kinetic model for the adsorption of metal cations
Table 3. Summary of different constants calculated from kinetic models used for the study
Pseudo-first-order
Metal
ions
k1
(min-1)
Qe (mg/g)
calculated R2
Qe
(mg/g)
Pb (II) 0.019 2.445 0.988
2.294
Cu (II) 0.024 1.774 0.986
1.684
Zn (II) 0.021 1.498 0.955
1.498
Cd (II) 0.024 1.372 0.966
1.319
Pseudo-second-order
Metal
ions
k2
(g/mg min)
Qe (mg/g)
calculated R2
h
(mg/g min)
Qe
(mg/g)
Pb (II) 0.003 4.085 0.847 0.015 2.294
Cu (II) 0.002 3.508 0.713 0.007 1.684
Zn (II) 0.002 3.390 0.491 0.005 1.498
Cd (II) 0.002 3.479 0.336 0.003 1.319
Intraparticle diffusion
Metal
ions
Kid
(mg/g min 0.5)
C
(mg/g) R2
Pb (II) 0.225 -0.172 0.934
Cu (II) 0.170 -0.184 0.928
Zn (II) 0.151 -0.189 0.916
Cd (II) 0.136 -0.173 0.903
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3.6.3. Intraparticle diffusion
The intraparticle diffusion model can be expressed by the equation as follows:
CtKQ idt 5.0 (6)
where Qt (mg/g) is the amount of adsorbate adsorbed at time t, Kid (mg/g min 0.5) is the intraparticle
diffusion constant and C is the adsorption constant. The graph of Qt against t0.5 produce the linear line
and the intraparticle diffusion constants Kid and C can be determined from the slope gradient and
intercept (Figure 11). The value of C indicates the thickness of boundary layer whereas the value of Kid
indicates the enhancement of adsorption.
Figure 11. Intraparticle diffusion model for the adsorption of metal cations
The reverse fitting graph of three kinetic models with experimental data can be seen in the
Figure 12. The pseudo-first-order model is found to be in better agreement with experimental data
compared to pseudo-second-order and intraparticle diffusion. Additionally higher correlation
coefficient values (Table 2) obtained from pseudo-first-order model also suggested the suitability of
pseudo-first-order model for present study. Moreover, it can also be concluded from the reverse fitting
graph and correlation coefficient values that intraparticle diffusion also takes part in controlling the
rate of reaction.
The results found are in agreement with previous studies reported in literature (Kalyani et al.,
2004; Hansen et al., 2006) where pseudo-first-order model was the rate limiting step.
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Figure 12. Reverse fitting graph of different kinetic models used in this study
4. Conclusions
Present study showed that UMRA observed better sorption performance at low initial metal ion
concentration from 5 to 20 mg/L. After increasing the initial metal concentration from 20 to 100 mg/L
the removal efficiency decreased from 14.3 % to 3.7 %, 12.4 % to 3.3 %, 11.4 % to 3.1 % and 10.9 %
to 2.9 % for Pb (II), Cu (II), Zn (II) and Cd (II) ions, respectively. The biosorption of Pb (II) ions were
higher than other metal ions. The isotherm studies revealed that Langmuir model better explained the
experimental data compared to Freundlich model. Maximum monolayer calculated adsorption capacity
for Pb (II), Cu (II), Zn (II) and Cd (II) ions were 3.99, 3.53, 3.43 and 3.23 mg/g, respectively. The
biosorption kinetics was best described by pseudo-first-order model. Equilibrium was achieved after
150 min of contact time. The experimental data demonstrated UMRA to be suitable media for use as
biosorbent in the removal of heavy metals from aqueous solutions.
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